TY - GEN
T1 - A quantitative assessment of SENSATIONAL with an exploration of its applications
AU - Xiong, Wei
AU - Song, Min
AU - Watrous-deVersterre, Lori
PY - 2010
Y1 - 2010
N2 - Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms, support vector machines (SVM) and K-means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K-means by 2% and 17% respectively. In addition, we develop a polysemy-based search engine and an experimental visualization application that utilizes SENSATIONAL clustering technique.
AB - Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms, support vector machines (SVM) and K-means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K-means by 2% and 17% respectively. In addition, we develop a polysemy-based search engine and an experimental visualization application that utilizes SENSATIONAL clustering technique.
UR - https://www.scopus.com/pages/publications/77957874657
UR - https://www.scopus.com/pages/publications/77957874657#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:77957874657
SN - 9781577354475
T3 - Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
SP - 289
EP - 294
BT - Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
PB - AAAI press
ER -